code: def forward(self, data):
      try:
          if "feat" in data.ndata:
              x = data.ndata["feat"]
          else:
              x = data.ndata["attr"]
      except:
          print("no x")
          pass
  
      # data = dgl.remove_self_loop(data)
      # data = dgl.add_self_loop(data)
  
      for i in range(self.num_layer - 1):
          x = self.convs[i](data, x).flatten(1)
          x = activate_func(x, self.args["act"])
  
      x = self.convs[-1](data, x).mean(1)
  
      return F.log_softmax(x, dim=1)
  
file_path: AutoGL/autogl/module/model/dgl/gat.py
imports: import torch
  import torch.nn.functional
  from dgl.nn.pytorch.conv import GATConv
  from .None import register_model
  from .base import BaseAutoModel, activate_func
  from ....utils import get_logger
label_desc: Use image node_id 2 for calling the GAT obj's underlying member method code with example usage: obj.forward(data) and returns: F